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Ananda Shikhara Bhat and Vishwesha Guttal: Read the article
Does evolution always favor individuals with higher fitness? Bhat & Guttal generalize standard equations of evolution to show that finite populations experience a novel force called noise-induced biasing which can reverse the direction of evolution predicted by natural selection
Researchers in evolutionary population biology have relied on mathematical equations and models to understand how traits spread and evolve in populations. These equations have been essential for understanding the complex relationship between natural selection and genetic variation. However, there has been a big catch. These classic models are built on the assumption that populations are large and steady, rarely reflecting the unpredictable and random fluctuations we observe in nature.
A new study by Bhat and Guttal builds on earlier work to deepen our understanding of evolution, showing that randomness—or “demographic stochasticity”—plays a significant role in shaping eco-evolutionary dynamics. By deriving general equations from first principles, their work synthesizes scattered findings and reveals that, in small populations, evolutionary outcomes can sometimes be the opposite of what is predicted by natural selection, highlighting the profound impact of demographic noise on evolutionary trajectories.
Traditionally, the replicator-mutator and Price equations have been the go-to tools for scientists modeling trait changes. However, these models fall short when dealing with real-world environments where population sizes can rise and fall unexpectedly. Bhat and Guttal’s approach extends these classic equations to account for randomness, transforming our understanding of how evolution plays out when populations aren’t just growing steadily in size but are also subject to wild, unpredictable shifts.
The research introduces two distinct ways in which randomness can shape evolutionary outcomes. One is a “direct” mechanism, which aligns with well-known concepts like bet-hedging, where species develop traits to survive in uncertain environments. The other is an “indirect” mechanism, revealing how evolutionary dynamics change in response to population density and frequency fluctuations. Together, these mechanisms paint a far more complex picture of how traits can evolve under the influence of demographic noise.
By offering a new lens through which to view the evolution of populations, Bhat and Guttal’s work challenges us to reconsider the balance between natural selection and the randomness of life. This research provides important insights into evolution and adaptation in small populations, such as those of rare or endangered species. As human-driven factors like climate change and habitat loss continue to reduce population sizes worldwide, understanding the role of demographic stochasticity becomes increasingly critical for predicting evolutionary trajectories. By incorporating randomness into models of population dynamics, this work has the potential to refine our understanding of evolution, ecology, and even evolutionary game theory.
Tahirah Williams is a PhD candidate in Quantitative and Systems Biology at the University of California Merced under the guidance of Dr. Clarissa Nobile. Her research focuses on understanding the molecular and genetic regulation of Coccidioides, the fungal pathogen responsible for Valley fever, and its interaction with the mucosal immune system. Her work will contribute to a better understanding of host-pathogen interactions. Tahirah enjoys traveling to new places, playing tennis, and cooking new dishes in her free time.
